Thomas George Thuruthel

Enrico Donato, Thomas George Thuruthel, Egidio Falotico

Autonomous systems face the intricate challenge of navigating unpredictable environments and interacting with external objects. The successful integration of robotic agents into real-world situations hinges on their perception capabilities, which involve amalgamating world models and predictive skills. Effective perception models build upon the fusion of various sensory modalities to probe the surroundings. Deep learning applied to raw sensory modalities offers a viable option. However, learning-based perceptive representations become difficult to interpret. This challenge is particularly pronounced in soft robots, where the compliance of structures and materials makes prediction even harder. Our work addresses this complexity by harnessing a generative model to construct a multi-modal perception model for soft robots and to leverage proprioceptive and visual information to anticipate and interpret contact interactions with external objects. A suite of tools to interpret the perception model is furnished, shedding light on the fusion and prediction processes across multiple sensory inputs after the learning phase. We will delve into the outlooks of the perception model and its implications for control purposes.

Enrico Donato, Egidio Falotico, Thomas George Thuruthel

Perception is essential for the active interaction of physical agents with the external environment. The integration of multiple sensory modalities, such as touch and vision, enhances this perceptual process, creating a more comprehensive and robust understanding of the world. Such fusion is particularly useful for highly deformable bodies such as soft robots. Developing a compact, yet comprehensive state representation from multi-sensory inputs can pave the way for the development of complex control strategies. This paper introduces a perception model that harmonizes data from diverse modalities to build a holistic state representation and assimilate essential information. The model relies on the causality between sensory input and robotic actions, employing a generative model to efficiently compress fused information and predict the next observation. We present, for the first time, a study on how touch can be predicted from vision and proprioception on soft robots, the importance of the cross-modal generation and why this is essential for soft robotic interactions in unstructured environments.

Thomas George Thuruthel, Syed Haider Abidi, Matteo Cianchetti et al.

Soft robotic grippers are shown to be high effective for grasping unstructured objects with simple sensing and control strategies. However, they are still limited by their speed, sensing capabilities and actuation mechanism. Hence, their usage have been restricted in highly dynamic grasping tasks. This paper presents a soft robotic gripper with tunable bistable properties for sensor-less dynamic grasping. The bistable mechanism allows us to store arbitrarily large strain energy in the soft system which is then released upon contact. The mechanism also provides flexibility on the type of actuation mechanism as the grasping and sensing phase is completely passive. Theoretical background behind the mechanism is presented with finite element analysis to provide insights into design parameters. Finally, we experimentally demonstrate sensor-less dynamic grasping of an unknown object within 0.02 seconds, including the time to sense and actuate.